diana                package:cluster                R Documentation

_D_I_v_i_s_i_v_e _A_N_A_l_y_s_i_s _C_l_u_s_t_e_r_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Computes a divisive hierarchical clustering of the dataset
     returning an object of class 'diana'.

_U_s_a_g_e:

     diana(x, diss = inherits(x, "dist"), metric = "euclidean", stand = FALSE,
           keep.diss = n < 100, keep.data = !diss)

_A_r_g_u_m_e_n_t_s:

       x: data matrix or data frame, or dissimilarity matrix or object,
          depending on the value of the 'diss' argument.

          In case of a matrix or data frame, each row corresponds to an
          observation, and each column corresponds to a variable.  All
          variables must be numeric. Missing values ('NA's) _are_
          allowed.

          In case of a dissimilarity matrix, 'x' is typically the
          output of 'daisy' or 'dist'.  Also a vector of length
          n*(n-1)/2 is allowed (where n is the number of observations),
          and will be interpreted in the same way as the output of the
          above-mentioned functions. Missing values (NAs) are _not_
          allowed. 

    diss: logical flag: if TRUE (default for 'dist' or 'dissimilarity'
          objects), then 'x' will be considered as a dissimilarity
          matrix.  If FALSE, then 'x' will be considered as a matrix of
          observations by variables. 

  metric: character string specifying the metric to be used for
          calculating dissimilarities between observations.
           The currently available options are "euclidean" and
          "manhattan".  Euclidean distances are root sum-of-squares of
          differences, and manhattan distances are the sum of absolute
          differences.  If 'x' is already a dissimilarity matrix, then
          this argument will be ignored. 

   stand: logical; if true, the measurements in 'x' are standardized
          before calculating the dissimilarities.  Measurements are
          standardized for each variable (column), by subtracting the
          variable's mean value and dividing by the variable's mean
          absolute deviation.  If 'x' is already a dissimilarity
          matrix, then this argument will be ignored.

keep.diss, keep.data: logicals indicating if the dissimilarities and/or
          input data 'x' should be kept in the result.  Setting these
          to 'FALSE' can give much smaller results and hence even save
          memory allocation _time_.

_D_e_t_a_i_l_s:

     'diana' is fully described in chapter 6 of Kaufman and Rousseeuw
     (1990). It is probably unique in computing a divisive hierarchy,
     whereas most other software for hierarchical clustering is
     agglomerative. Moreover, 'diana' provides (a) the divisive
     coefficient (see 'diana.object') which measures the amount of
     clustering structure found; and (b) the banner, a novel graphical
     display (see 'plot.diana').

     The 'diana'-algorithm constructs a hierarchy of clusterings,
     starting with one large cluster containing all n observations.
     Clusters are divided until each cluster contains only a single
     observation.
      At each stage, the cluster with the largest diameter is selected.
     (The diameter of a cluster is the largest dissimilarity between
     any two of its observations.)
      To divide the selected cluster, the algorithm first looks for its
     most disparate observation (i.e., which has the largest average
     dissimilarity to the other observations of the selected cluster).
     This observation initiates the "splinter group". In subsequent
     steps, the algorithm reassigns observations that are closer to the
     "splinter group" than to the "old party". The result is a division
     of the selected cluster into two new clusters.

_V_a_l_u_e:

     an object of class '"diana"' representing the clustering.  See
     '?diana.object' for details.

_S_e_e _A_l_s_o:

     'agnes' also for background and references; 'diana.object',
     'daisy', 'dist', 'plot.diana', 'twins.object'.

_E_x_a_m_p_l_e_s:

     data(votes.repub)
     dv <- diana(votes.repub, metric = "manhattan", stand = TRUE)
     print(dv)
     plot(dv)

     data(agriculture)
     ## Plot similar to Figure 8 in ref
     ## Don't run: plot(diana(agriculture), ask = TRUE)

